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Orion AI Factory

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Sovereign AI infrastructure in Serbia for developing, training, deploying, and storing AI models on NVIDIA B200 GPUs, with zero egress fees, 1–2 ms latency, and pay-as-you-go plans.

About Orion AI Factory

Orion AI Factory is a sovereign AI infrastructure platform in Serbia for developing, training, deploying, and storing AI models on NVIDIA B200 Blackwell GPUs. It provides locally hosted AI infrastructure with pay-as-you-go GPU access, zero egress fees, deployment advertised within 24 hours, and direct access to engineering teams.

What it does

Provides GPU infrastructure for AI development, debugging, fine-tuning, training, inference, deep research, and high-scale inference.

  • Combines AI development, training, inference, and model storage in a single infrastructure environment.
  • Supports deployment and processing of AI workloads in Serbia without reliance on foreign cloud regions.
Key features
  • NVIDIA B200 Blackwell GPUs with 192 GB VRAM per GPU.
  • GPU-hour billing with Entry, Pro, Max, and Enterprise packages.
  • Zero egress fees and local data processing.
  • Deploy in 24 hours with no waitlist, according to the homepage.
  • InfiniBand interconnect described as AI optimized.
  • NetApp high-performance storage.
  • Optional 1 TB storage add-on.
  • Enterprise package includes NVIDIA AI Enterprise software and premium support.
  • Direct access to the engineering team.
  • Orion telekom is identified as an official NVIDIA partner through the NVIDIA Partner Network. What makes it different Orion AI Factory is positioned as a sovereign AI factory rather than a server-rental service. Its infrastructure is located in Serbia, with data location and processing governed locally, while offering NVIDIA Blackwell hardware, transparent GPU-hour billing, zero egress fees, and direct engineering access. The homepage also states 1–2 ms latency and emphasizes local control, performance, and legal certainty compared with foreign cloud regions.

Use Cases

  • 1.Developing and debugging AI models on fractional GPU plans
  • 2.Fine-tuning and training medium or large models
  • 3.Deploying inference workloads and conducting deep research

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